def setUp(self):
self.id = False
self.uv = False
self.ub = False
self.threshold = 0.8
self.assoc_threshold = 0.3
seed = 10
dim = 512
prop = 0.1
input_dir, output_dir = tools.read_config()
relation_symbols = symbol_definitions.uni_relation_symbols()
vector_factory = VectorFactory(seed)
isA_symbols = symbol_definitions.isA_symbols()
sentence_symbols = symbol_definitions.sentence_role_symbols()
self.corpus_dict, self.id_vectors, self.semantic_pointers = \
tools.setup_corpus(
input_dir, relation_symbols, dim, vector_factory,
seed, self.id, self.uv, prop)
self.createAssociator(self.id_vectors, self.semantic_pointers)
self.tester = WordnetAssociativeMemoryTester(
self.corpus_dict, self.id_vectors, self.semantic_pointers,
relation_symbols, self.associator, seed, output_dir, isA_symbols,
sentence_symbols, VectorFactory(), self.uv, True)
def setUp(self):
self.id = False
self.uv = False
self.ub = False
self.threshold = 0.8
self.assoc_threshold = 0.3
seed = 10
dim = 512
prop = 0.1
input_dir, output_dir = tools.read_config()
relation_symbols = symbol_definitions.uni_relation_symbols()
vector_factory = VectorFactory(seed)
isA_symbols = symbol_definitions.isA_symbols()
sentence_symbols = symbol_definitions.sentence_role_symbols()
self.corpus_dict, self.id_vectors, self.semantic_pointers = \
tools.setup_corpus(
input_dir, relation_symbols, dim, vector_factory,
seed, self.id, self.uv, prop)
self.createAssociator(self.id_vectors, self.semantic_pointers)
self.tester = WordnetAssociativeMemoryTester(
self.corpus_dict, self.id_vectors, self.semantic_pointers,
relation_symbols, self.associator, seed, output_dir, isA_symbols,
sentence_symbols, VectorFactory(), self.uv, True)
def __init__(self,
dimension=512,
input_dir="wordnet_data",
unitary_relations=False,
proportion=1.0,
num_synsets=-1,
id_vecs=True,
relation_symbols=None,
create_namedict=False,
dry_run=False,
sp_noise=0,
normalize=True):
self.dimension = dimension
self.input_dir = input_dir
self.unitary_relations = unitary_relations
self.create_namedict = create_namedict
self.normalize = normalize
if sp_noise < 0:
sp_noise = 0
self.sp_noise = sp_noise
if relation_symbols is None:
self.relation_symbols = symbol_definitions.uni_relation_symbols()
else:
self.relation_symbols = relation_symbols
self.parse_wordnet()
self.proportion = (float(num_synsets) / len(self.corpus_dict)
if num_synsets > 0 else proportion)
if self.proportion < 1.0:
self.create_corpus_subset(self.proportion)
print "Wordnet data parsed."
if dry_run:
print "Dry run. Skipping vectorization."
else:
self.form_knowledge_base(id_vecs, unitary_relations)
print "Vectorization of WordNet complete"
def __init__(self, dimension=512, input_dir="wordnet_data",
unitary_relations=False, proportion=1.0, num_synsets=-1,
id_vecs=True, relation_symbols=None, create_namedict=False,
dry_run=False, sp_noise=0, normalize=True):
self.dimension = dimension
self.input_dir = input_dir
self.unitary_relations = unitary_relations
self.create_namedict = create_namedict
self.normalize = normalize
if sp_noise < 0:
sp_noise = 0
self.sp_noise = sp_noise
if relation_symbols is None:
self.relation_symbols = symbol_definitions.uni_relation_symbols()
else:
self.relation_symbols = relation_symbols
self.parse_wordnet()
self.proportion = (float(num_synsets)/len(self.corpus_dict)
if num_synsets > 0 else proportion)
if self.proportion < 1.0:
self.create_corpus_subset(self.proportion)
print "Wordnet data parsed."
if dry_run:
print "Dry run. Skipping vectorization."
else:
self.form_knowledge_base(id_vecs, unitary_relations)
print "Vectorization of WordNet complete"
